Dynamic response pattern of gold prices to economic policy uncertainty

Based on a time-varying parameter structural vector autoregression with stochastic volatility(TVP-SVAR-SV)model,the time-varying effects and country differences of economic policy uncertainty(EPU)on gold prices from August 2006 to December 2017 were examined.The results show that the effects of global economic policy uncertainty(GEPU)shock on gold prices change over time.The changes were positive during 2006-2008 and 2013-2017,while the impacts were negative during 2009-2012,implying that the efficiency of gold as a safe haven is not stable and depends on economic conditions.There are significant country differences regarding the impact of EPU on the price of gold,particularly during the international financial crisis,European debt crisis and Trump election.During the international financial crisis,EPU exerts a positive impact on gold prices in most countries.During the European debt crisis,the impact of EPU on gold prices is mainly negative in the examined countries.While during the Trump election,the impact displays positive and negative alternating in most countries.

Gold Price Prediction Based on PCA-GA-BP Neural Network

Gold price is affected by a variety of factors and has highly nonlinear and random features. Some traditional forecast methods emphasize linear relations excessively and some ignore the price randomness. The predictive error is relatively large. Therefore, a BP neural network model based on principal component analysis (PCA) and genetic algorithm (GA) was proposed for the short-term prediction of gold price. BP could establish the gold price forecasting model. The weights and thresholds of BP neural network are optimized by GA, which overcome the shortcoming that BP algorithm falls into local minimum easily. PCA can effectively simplify the network input variables and speed up the convergence. The results showed that, compared with GA-BP and BP, the convergence rate of PCA-GA-BP neural network model was faster and the prediction accuracy was higher in the prediction of gold price.

Price Dynamics of the World Gold Market： A Model Incorporating Inventories

Gold is regarded as a strategic mineral in many countries and its price is a key indicator of global business confidence. There is need for dynamic modelling of the world gold market, which would enhance understanding of the world market conditions, especially the long-term tendency of world gold prices, and hence facilitate long-term planning. This study incorporates inventories into the world market model and uses simultaneous equation approaches to estimate the model. From this estimation, the paper derives the time-path for the world annual price of gold. Results show that the price time-path converges without oscillations, from below, towards an intertemporal equilibrium. This equilibrium is estimated at about US$105,000.00 per kilogram based on a projected average world income. If the assumption of average income is relaxed, the intertemporal equilibrium price becomes variable dependent on the actual values of world income at a given time, which however, does not alter its dynamic characteristics. The results, therefore, show that gold price is dynamically stable. Short-term fluctuations, which are sometimes extreme, have no long-term effect on gold attractiveness.

Gold Prices as a Mechanism of Control and Equilibrium in Financial Markets

Gold is used as a currencies comparative measure and,because of its properties(it does not rust)and use(in space industry,for example),it has a significant role in balancing both financial markets and economies.During crises,gold seldom loses value.We aim to show that price of gold is a stabilizing factor for the economic balance.We will do so utilizing the chaos theory,which gains more and more popularity in social sciences.

The Most Significant Factors Influencing the Price of Gold： An Empirical Analysis of the US Market

Gold is always a precious metal for many hundred years. Semi flexible gold demand and supply chain determines international gold prices in the long term. USA is ranked the world’s largest gold producer. This study mainly aims to investigate the dynamic factors which affect the price of gold and determine the essential macro-economic variable that has the most important role during the process. This paper examines USA over 13 years applying a formal test for time series, which interrogate cointegration relationships, what is the affiliation between gold price and other factors, which are explained in detail below. The present study has used the monthly data from January, 2003 to June, 2016. Databases are provided by the Federal Reserve, the central bank of the United States, and United States Energy Information Administration. Data analysis was performed with software package EViews 8. Through the time series, an analysis has been carried out on Dow Jones Index, the US exchange rate, silver price, interest rate, oil price and inflation rate which are thought to influence the price of gold in the most significant way. The data analysis includes the determination of the conditional heteroscedastic model to estimate volatility. Therefore, the best fitting model to the data set, which is the exponential GARCH model, is preferred. In accordance with the results of the empirical analyses in the USA, the highest negative correlation is found between gold prices and US exchange rate. Secondly, a positive correlation is found among gold prices, silver prices, and oil prices. Another point which takes attention as a result of the study is that economic and political structural breaks weighed heavily, traders and hedgers from all over the world were able to drive prices up to incredible highs. The added valueof our study arises from the inclusion in the analysis of macro economic variables, which has proved to have crucial relevance for the price of gold in the context of the recent economic structure.

Analysis on the influence factors of Bitcoin’s price based on VEC model

Background:Bitcoin,the most innovate digital currency as of now,created since 2008,even through experienced its ups and downs,still keeps drawing attentions to all parts of society.It relies on peer-to-peer network,achieved decentralization,anonymous and transparent.As the most representative digital currency,people curious to study how Bitcoin’price changes in the past.Methods:In this paper,we use monthly data from 2011 to 2016 to build a VEC model to exam how economic factors such as Custom price index,US dollar index,Dow jones industry average,Federal Funds Rate and gold price influence Bitcoin price.Result:From empirical analysis we find that all these variables do have a long-term influence.US dollar index is the biggest influence on Bitcoin price while gold price influence the least.Conclusion:From our result,we conclude that for now Bitcoin can be treated as a speculative asset,however,it is far from being a proper credit currency.

Market efficiency of gold exchange-traded funds in India

Background:Gold exchange-traded funds,since introduction,are primarily aimed at tracking the price of physical gold in the financial market.This,a category of exchange-traded funds,whose units represent physical gold,is traded on exchanges like any other financial instrument.In the Indian financial market,gold exchange traded funds were introduced a decade ago to facilitate ordinary households'participation in the bullion market.They were also designed to assist in the price discovery mechanism of the bullion market.Presentation of the hypothesis:In this paper,it is attempted to check if one of the constituents of price discovery mechanism,informational efficiency,has been achieved in gold exchange-traded funds’market.Information efficiency becomes evident only when all available information is reflected in the market price of the instrument.Testing the hypothesis:Therefore,in order to assess the weak-form efficiency of the gold exchange-traded funds market,the daily returns of five gold exchangetraded funds traded on the Indian Stock Exchange over the period March 22,2010,to August 28,2015,were used.The non-parametric runs test,the parametric serial correlation test,and the augmented Dickey-Fuller unit root test are employed.Implications of the hypothesis:The test results provide evidence that the efficient market hypothesis does not hold for the gold exchange-traded funds’market in India.Further,the test results address several underlying issues with respect to price discovery in the market under study and suggest that the Indian market for this derivative is not weak-form efficient.Hence,the factors affecting gold exchange traded-funds’market warrant the attention of the country’s regulatory bodies,as appropriate legislation in support of market efficiency is needed.

Investigating seasonality,policy intervention and forecasting in the Indian gold futures market:a comparison based on modeling non‑constant variance using two different methods

This study focuses on the Indian gold futures market where primary participants hold sentimental value for the underlying asset and are globally ranked number two in terms of the largest private holdings in the physical form.The trade of gold futures relates to seasons,festivity,and government policy.So,the paper will discuss seasonality and intervention in the analysis.Due to non-constant variance,we will also use the standard variance stabilization transformation method and the ARIMA/GARCH modelling method to compare the forecast performance on the gold futures prices.The results from the analysis show that while the standard variance transformation method may provide better point forecast values,the ARIMA/GARCH modelling method provides much shorter forecast intervals.The empirical results of this study which rationalise the effect of seasonality in the Indian bullion derivative market have not been reported in literature.

Linking gold prices,fossil fuel costs and energy consumption to assess progress towards sustainable development goals in newly industrialized countries

The continuous rise in global environmental challenges has led to urgency toward establishing a secure framework to achieve sustainable development goals.This study establishes a novel theoretical framework to analyze the role of energy prices,energy consumption,gold prices and economic growth on environmental degradation in newly industrialized economies.To realize sustainable development goals and foster environmental defence,this study utilizes CS-ARDL as the main econometric approach to investigate the asymmetric association between environmental degradation and relevant factors.We also use AMG,CS-DL,Driscoll-Kray and FGLS to enhance the robustness of our findings.Our econometric approach reveals that energy resource prices and renewable energy consumption reduce environmental degradation,while gold prices and fossil energy consumption elevate environmental pollutants.We also confirm the existence of the EKC hypothesis.The findings of our extensive analysis paved the way for a welldesigned environmental policy for NIC economies should focus on renewable energy consumption,green investments,and structural changes.

基于隐马尔可夫模型的黄金期货价格趋势研究

本文基于隐马尔可夫模型(HMM)探究黄金期货价格的变动趋势,通过麻雀搜索算法(SSA)对HMM模型初始状态的概率分布进行优化,解决HMM模型容易陷入局部最优的缺陷,得到了改进的模型(SSA-HMM)。文章通过实际应用比较证明了SSA-HMM在黄金期货价格趋势预测中是有效的,同时深入探究HMM模型隐状态与实际问题之间的联系,论证了结合期货价格波动的驱动因素对隐马尔科夫模型的隐状态进行描述是更加合理的。

RNN在线学习框架下CNN-LSTM模型对黄金期货价格的预测

黄金是一种特殊的金融商品,具有避险功能。黄金期货价格受多方面因素的影响,一般认为黄金期货价格变化趋势呈现非线性非平稳的时间序列,传统的预测模型难以对其进行有效的预测。文章向传统在线学习算法中加入信息传递,提出基于RNN的在线学习算法ROA(RNN-based Online Algorithm);选用芝加哥商品交易所黄金期货价格数据进行实证分析,使用CNN-LSTM作为基础预测模型,以MAE、RMSE、R^(2)作为评价指标,结果表明在所有评价指标中ROA的预测性能均优于传统在线学习算法。

我国金融形势指数的构建与混频预测

近年来,我国黄金和成品油的市场需求有所升温,本文尝试探讨是否应将黄金价格和西德克萨斯轻质(WTI)原油价格纳入构建我国金融形势指数。研究表明,纳入黄金价格的我国金融形势指数能够更好地拟合居民消费价格指数(CPI)的演进路径,且在时序上领先CPI三个季度;但纳入WTI原油价格不仅加剧了金融形势指数的波动,还弱化了其与CPI的关系。通过构建16变量混频贝叶斯向量自回归(MF-BVAR)模型,并综合运用点预测、区间预测、密度预测三种方法,对比混频贝叶斯向量自回归模型与季频贝叶斯向量自回归(QF-BVAR)模型、季频向量自回归(QF-VAR)模型对金融形势指数的预测能力。实证结果显示,MF-BVAR模型对于金融形势指数的预测效果优于QF-BVAR和QF-VAR模型,且该结论不受预测期的影响。季度内不同月份的信息差异对金融形势指数预测产生显著影响,且随着预测期的增加,不同组别之间金融形势指数的预测能力逐渐趋同。本研究对于加强金融形势预测和防范金融市场风险具有理论价值与实践意义。

全球政治、经济不确定性与黄金价格波动--基于GARCH-MIDAS模型的实证研究

从政治和经济不确定性两个维度入手构建GJR-GARCH-MIDAS模型,探讨全球不确定性对国际黄金价格波动的溢出效应及其传导路径,并对黄金市场的未来价格波动趋势进行考察。(1)全球政治、经济不确定性对黄金价格波动产生显著正向冲击,纳入不确定性因素的双因子GARCH-MIDAS模型对后者的解释效果较佳。(2)不同政治、经济不确定性子要素对黄金市场的冲击强度和冲击时间具有异质性;地缘政治行动和货币政策不确定性是影响黄金价格长期波动的最主要因素。(3)外部不确定性主要通过影响黄金的商品属性、资产属性、准货币属性对黄金市场施加影响,其中“准货币属性”是近年来不确定性冲击影响黄金市场波动的最主要路径。(4)模型预测结果显示,随着全球政治、经济不确定性因素的减弱和反复,黄金价格长期波动成分从2023年第二季度开始,将逐步下移,随后在第四季度有所反弹。

基于人工神经网络算法的黄金价格预测问题研究

提出一种结合宏观国际经济影响因素与黄金价格时间序列相接合的LM-BP模型。仿真结果表明,该算法能够较好预测黄金价格的走势,并且运用固定权值阈值方法实现了预测结果的稳定,使得能够进行实际应用。

黄金价格与通货膨胀相关性的实证分析

在考察1996—2007年中国通货膨胀和黄金价格波动情况的基础上,应用扩展菲利普斯曲线方程和最小二乘估计法对黄金价格与中国通货膨胀的相关性进行了实证分析。分析结果表明,黄金价格对未来CPI具有一定的预测作用,即可以用黄金价格作为一种参考指标,判断未来的经济走势和通货膨胀变化趋势。同时,黄金凭借其商品和货币的双重属性具有抵抗通货膨胀的特性,据此提出了中国应适时、适度增加黄金储备,发展黄金市场,完善金融体系的建议。

国际黄金价格与美元指数关系的实证分析

本文采用2009年1月至2013年12月数据,运用计量金融学分析方法,在对时间序列数据进行单整检验、协整检验,确定数据平稳性和变量之间长期关系的基础上,构建向量自回归(VAR)模型,利用脉冲响应函数和Granger因果检验对国际黄金价格与美元指数之间的关系进行分析。结果表明,美元指数与国际黄金价格之间不仅存在负的长期均衡关系,而且在Granger意义上因果相关,即美元指数上升会引起国际黄金价格下降。

小波神经网络在黄金价格预测中的应用

通过对影响黄金价格变动的主要因素的研究,提出一种基于小波神经网络的黄金价格预测模型。给出了具体的网络学习算法,并结合算法对黄金价格进行预测。为验证模型有效性,进行了对比测试。分析结果表明,小波神经网络模型比传统的BP神经网络模型具有收敛速度快、预测精度高的特点。

影响黄金价格因素及应对策略

黄金价格一直被人们所关注。影响黄金价格因素是多方面的,如石油价格、美元汇率、美国联邦储备委员会的利率政策、国际政治局势等,其中最具有影响力的因素是国际石油价格和国际政治事件。通过分析表明,这两个因素对黄金价格走势的引导和震荡作用明显。针对这一情况,提出了我国应对策略,即确定合理的黄金储备规模,从勘探、开采、技术、结构中抓效益。这些策略在一定程度上会对稳定我国经济发展状况、提高国际资信度等方面起到极大的作用。

基于ARMA-GARCH模型的黄金价格实证分析

研究黄金价格的动态演变过程至关重要。文中以1971年1月至2008年12月期间的伦敦黄金交易市场下午定盘价格为基础,利用时间序列的相关理论,建立了黄金价格的ARMA-GARCH模型,并对2008年数据进行了实证分析,其结果非常接近。利用该模型可动态刻画黄金价格数据的生成过程,也可帮助黄金产品投资者和生产者做出更加灵活、科学的决策。

黄金白银投资比较及其价格影响因素分析

通过分析目前黄金与白银各自发展的空间优势,本文研究了影响黄金白银价格的几大重要因素,以及黄金价格与白银价格的线性关系,提出影响它们价格的主要因素包括石油价格、美国名义有效汇率、美国长期利率和美国消费者价格指数、美元指数,以及世界黄金储备会对黄金和白银价格的影响,并得出其各自较好的持有途径,旨在为投资决策者提供参考。